How enterprises are rethinking collaborative analytics

A persistent gap among C-suite executives shows that enterprises need to rethink traditional data ownership models as they move toward shared decision-making frameworks that better align their analytics strategy and business goals. Why? Simply asking who owns the data isn’t the right question, especially for leaders who want analytics to become a meaningful aspect of a collaborative, strategic management culture.

At first glance, a data ownership model has its advantages. With centralized data teams and clear ownership for each data point within an organization, leaders can ensure greater control and compliance in how data is used and modeled. Strong CIO leadership can also enable consistency in data use.

Despite these strengths, however, data ownership significantly limits the possibilities of true collaboration and partnership. An ownership focus creates data silos that often leave certain pieces of information inaccessible to those who could benefit from it.

Each department’s view of the business becomes more isolated, and external partners can similarly struggle with a lack of consistent and accurate data support. The end result is a system that hinders innovation and slows decision-making because analytics is just another service to manage, rather than a true partner.

In contrast, Gartner research indicates that companies with successful data-sharing initiatives are 1.7 times more effective at showing business value and ROI from their data analytics strategy. Plus, leaders see their roles mature beyond data and analytics to engage in digital transformation and overall business strategy.

And as the number of data sources businesses need to work with explode — 41% of enterprises now manage over 500 petabytes of data — a fixation on ownership alone becomes increasingly out of date. Data needs to be leveraged in real time to drive outcomes, rather than just being part of a quarterly report. And as more activities inherently involve joint responsibility for data, it further drives the need for shared ownership and decision-making.

Case in point

George Thomas, global CIO for JLL, explains how the Fortune 500 commercial real estate services firm navigates the shift. “We’ve moved from data ownership to collaborative decision-making underpinned by an enterprise data platform integrated from diverse real estate data sources,” he says. “The partnership between our data and business teams has shortened cycle times, enhanced compliance, and enabled our sales teams to leverage insights from client services and building operations. And our framework is intentionally designed for commercial real estate’s complexity: teams use shared market intelligence within disciplined governance. This is the foundation to scale AI responsibly by delivering deeper insights and recommendations to clients while improving how we operate.”

With a shared decision framework, companies are able to become more fully aligned on their overall analytics strategy, as this approach naturally fosters more cross-functional collaboration driven by a holistic view of the enterprise’s needs, rather than simply focus on an individual department.

Facilitating the new model

For executives, enabling a shared decision-making model requires more of a custodian or stewardship approach to data. There’s still someone primarily in charge of the data used by collaborators, but rather than limiting data access, their responsibility is to provide clean, cataloged data to those who need it.

“But that doesn’t happen without fundamental systems in place,” says Omri Kohl, founder and CEO of Pyramid Analytics. “As analytics needs grow, it’s essential that organizations have access to unified metrics management and insight engines so diverse teams can work together without operations becoming more complex. Also, strong governance models and tech tools are critical to get everyone fully aligned in a shared decision-making model.”

This unified data approach becomes especially important as enterprises increasingly make use of AI agents in their workflows. The average enterprise utilizes 275 SaaS applications, resulting in even more data inputs that are often locked behind silos of their own. This limits real-time visibility and the ability to generate meaningful operational insights. So agentic AI that can bring relevant data into a unified and accessible platform is critical.

With the right technology foundation in place, enterprises can begin to make analytics part of a truly collaborative ecosystem, where data and insights are more easily shared across departments, data teams, and other invested partners. This approach also enables a more holistic view in defining success in relation to analytics. Organizations can move beyond simply worrying about whether they use dashboards or get accurate data, and instead focus on more meaningful outcomes like the impact a decision has on the business.

Becoming true partners

The right tech systems are essential for stronger analytics partnerships, but equally important is that leaders redefine the existing culture surrounding their approach to analytics. This shift can occur both with external vendors and partners, as well as within internal teams making use of the same data sets.

This starts at the top, with leadership clearly communicating how an analytics strategy is going to be reframed around decisions and their outcomes, rather than focusing exclusively on the data itself. This may also involve a shift with external partners — looking at these relationships as more than just someone who supplies a tool, and working closely together to create a Vested partnership focused on enabling shared goals through better data governance.

John Deere may not be the first company that comes to mind with data analytics, yet the farm equipment manufacturer has spent years developing systems that allow back and forth data sharing between the company and the farmers who use its equipment.

Shared data has been used to inform the development of new products and services, improve farmers’ abilities to analyze equipment performance, and enable better remote diagnostics and predictive maintenance. The more the data ownership mindset gets broken down, the easier it becomes for John Deere and farmers to get mutually beneficial outcomes from their data.

The shared decision-making approach to analytics partnerships still requires guardrails, compliance oversight, and a mindful approach to trust and ethics, especially when external partners are involved. But by creating a system that promotes stronger alignment and cross-functional collaboration, analytics partnerships can achieve better results for all.

Making analytics a shared commitment

Moving from a data ownership focus to a shared mindset allows enterprises to realize the full potential of their analytics partnerships. When internal and external partners have full access to the data they need, they can make better decisions together and gain a meaningful competitive advantage as they make progress toward shared KPIs. By implementing the operational and cultural shifts to make this happen, CIOs can set their organizations up for faster innovation, more data transparency, and better decision making.